Investigating stress level based on psychophysiological signals

In psychophysiology research, we assign a psychological meaning to the responses of the body's physiology according to factors such as the quality of the experiment design, the psychological characteristics of the measurements, and the appropriateness of the analysis and interpretation of the data. In the analysis of reactions, none of the two sciences of physiology and psychology are superior, but complement each other. Understanding different mental states, including stress states, which have known destructive effects on the human body and mind, are one of the important applications of this science. In this research, by presenting a suitable experiment and creating three levels of stress (low, medium and high) in the subject and recording plethysmograph signals, changes in heart rate and electrical conductivity of the skin, we sought to obtain a measure to quantify the individual's stress level. For this purpose, pre-processing and various linear processing in the field of time, frequency and time-frequency and non-linear including Poincare criterion, Lyapunov exponent, fractal dimension and entropy and extracting various features from the recorded signals. There have been. Then, by using different classification methods such as neural network combination and genetic algorithm, support vector machines and linear combination function method, different levels have been separated. In this research, the optimal characteristics of each signal were first determined and separated by these three methods. Then, by combining the optimal features of all signals, separation was done again. Finally, it was concluded that using the HRV signal alone can achieve higher results in the accuracy of separation. Next, a comparison was made between the linear and non-linear characteristics of the HRV signal and it was concluded that the combination of these two types of characteristics improves the results.

Keywords:

Psychophysiology , stress levels , photoplethysmography signal.